PLoS Computational BiologyBiophysicsPeer-reviewed journal article
BIOPOINT: A particle-based model for probing nuclear mechanics and cell-ECM interactions via experimentally derived parameters
Sandipan ChattarajiD, Julius ZimmermanniD, Francesco S. PasqualiniiD
Abstract
Morphogenesis involves biochemical and biomechanical interactions across multiple spatial and temporal scales. Experimental studies alone struggle to resolve these dynamics, necessitating computational models. Among these, subcellular element modeling (SEM) has proven helpful in simulating cellular and tissue-scale emergent behaviors. However, traditional SEM frameworks lack explicit representations of nuclear mechanics and cell-extracellular matrix (ECM) interactions, limiting their ability to capture key biology. Here, we introduce BIOPOINT, a particle-based computational framework that extends SEM by incorporating (1) a deformable, multi-particle nucleus to simulate nuclear stress and strain distributions and (2) an explicit ECM representation using a structured array of static particles interacting via tunable adhesive potentials. To ensure biological relevance, we calibrated BIOPOINTs parameters against single-cell indentation experiments, overcoming prior limitations of ad hoc parameter selection in SEM. We validate BIOPOINT by comparing simulated cell behaviors to experimental observations in three key scenarios: (i) single-cell indentation, demonstrating agreement with force-time curves from atomic force microscopy (AFM) studies; (ii) cell spreading on ECM micropatterns, confirming that nuclear deformation follows ECM constraints; and (iii) nuclear deformation during confined migration, showing BIOPOINT predicts nuclear shape dynamics as cells traverse constrictions accurately. BIOPOINT provides a computational framework for simulating nuclear mechanics and cell-ECM interactions with experimentally derived parameters. By integrating experimental data with a particle-based approach, BIOPOINT offers a practical tool for studying cell behavior that can inform future morphogenetic studies in-vivo or in-vitro.
- assay: imaging
- research focus: cell signaling & trafficking
- research focus: development & morphogenesis
- approach: deep learning
- approach: statistical model
- Citations
- 1
- FWCI
- 0.00
- Field percentile
- 18th percentile
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- SEM2: A computational framework to model multiscale mechanics with subcellular elements
S. Chattaraj, M. Torre, … F. Pasqualini·bioRxiv·Jul 7, 2023
- A multi-structural finite element model to simulate atomic force microscopy nanoindentation of single cells
S. Marcotti, G. C. Reilly, D. Lacroix·bioRxiv·Dec 17, 2019
- Modelling the mechanical cross-talk between cells and fibrous extracellular matrix using hybrid cellular Potts and molecular dynamics methods
E. Tsingos, B. H. Bakker, … R. M. H. Merks·bioRxiv·Jun 10, 2022
- In silico mechanobiochemical modeling of morphogenesis in cell monolayers
Bahador Marzban, Xiao Ma, … Hongyan Yuan·bioRxiv·Feb 13, 2018
- Mechanics and buckling of biopolymeric shells and cell nuclei
E. J. Banigan, A. D. Stephens, J. F. Marko·bioRxiv·Oct 2, 2017
- Calculation of the force field required for nucleus deformation during cell migration through constrictions
I. D. Estabrook, H. R. Thiam, … R. J. Hawkins·bioRxiv·Dec 17, 2020